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Infrenic builds AI predictive maintenance apps on Keel. The app reads vibration, temperature and PLC data through an edge gateway in your plant, learns what normal looks like for each machine, and flags the pattern that comes before a failure. It then opens the maintenance ticket, orders the part and tells the shift lead.

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What you get

  • Less unplanned downtime

    Problems are flagged early enough to fix them in a planned stop instead of mid-shift.

  • Uses your existing sensors

    Reads PLCs and sensors over OPC UA, Modbus or MQTT; add-on sensors only where data is missing.

  • Action, not just alerts

    Tickets, parts orders and shift notes are created for you, with the readings attached.

  • Engineers approve

    The app recommends; your maintenance team decides, and every decision is logged.

How it works

  1. 01

    Pick one machine type and its failure history

  2. 02

    Connect its sensors through the edge gateway

  3. 03

    See early warnings on your data within 48 hours

  4. 04

    Roll out to the line and keep improving

Built on Keel

Every Infrenic app runs on Keel, our engine for agents, knowledge, connectors, approvals and monitoring. It also connects to your hardware.

How Keel worksHardware integration

FAQ

Questions

A few weeks of sensor readings is enough to start, and failure records help. Without failure history the app starts by flagging unusual behaviour and improves as your team confirms what matters.

Yes, if the machine or its controller exposes data. Where it does not, a small vibration or current sensor added to the machine usually gives enough signal.

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